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Content warning: This work displays examples of explicit and strongly offensive language. The COVID-19 pandemic has fueled a surge in anti-Asian xenophobia and prejudice.Many have taken to social media to express these negative sentiments, necessitating the development of reliable systems to detect hate speech against this often under-represented demographic. In this paper, we create and annotate a corpus of Twitter tweets using 2 experimental approaches to explore anti-Asian abusive and hate speech at finer granularity. Using the dataset with less biased annotation, we deploy multiple models and also examine the applicability of other relevant corpora to accomplish these multi-task classifications. In addition to demonstrating promising results, our experiments offer insights into the nuances of cultural and logistical factors in annotating hate speech for different demographics. Our analyses together aim to contribute to the understanding of the area of hate speech detection, particularly towards lowresource groups.

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The Children's Data Network (CDN) is a data and research collaborative focused on the linkage and analysis of administrative records. In partnership with public agencies, philanthropic funders, affiliated researchers, and community stakeholders, we seek to generate knowledge and advance evidence-rich policies that improve the health, safety, and well-being of the children of California. Given our experience negotiating access to and working with existing administrative data (and importantly, data stewards), the CDN has demonstrated its ability to perform cost-effective and rigorous record linkage, answer time-sensitive policy- and program-related questions, and build the public sector's capacity to do the same. Owing to steadfast and generous infrastructure and project support, close collaboration with public partners, and strategic analyses and engagements, the CDN has promoted a person-level and longitudinal understanding of children and families in California and in so doing, informed policy and program development nationwide. We sincerely hope that our experience—and lessons learned—can advance and inform work in other fields and jurisdictions.

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